The authors have nothing to disclose.

The protocol was approved by the institutional review board at the University of Wisconsin-Madison. The following steps have been written as a guide for programmers to replicate the automated process of the web application described.
1. Login Participant
<ol>
	<li>Instruct the participant to use an internet-enabled computer and navigate to the web application using an HTML5 compatible browser: <a href="http://brainandlearning.org/jove" target="_blank">http://brainandlearning.org/jove</a>. Have the participant sit in a quiet room free of distractions, with the computer at a comfortable height. 
	NOTE: Since the entire experiment is hosted online, the tasks can also be performed remotely without the presence of a research assistant. All instructions for the participant are included in the web application.</li>
	<li>Have the participant input a unique ID that will be associated with the data collected and stored in a MySQL database. Have the participant reuse this ID if the online tasks are not completed within the same session. Before logging in, obtain consent from the participant via a consent form linked on the page. 
	NOTE: A participant&#8217;s progress is saved after each task in order to allow for completion of the 2 tasks at separate times if needed. Instruct the participant to always use the same ID in order to start where one left off.</li>
</ol>
2. Screen Calibration
NOTE: The web application guides the participant through the three steps outlined in the calibration page at: <a href="http://brainandlearning.org/jove/Calibration" target="_blank">http://brainandlearning.org/jove/Calibration</a>.
<ol>
	<li>Ask the participant to input the diagonal size of the screen in inches in the labeled textbox.
	<ol>
		<li>However, if the participant does not know this information, have the participant find a CD or credit card as a calibration object (Figure 1). When one is selected, prompt the participant to place the object against the screen and align it with a representative image of the object displayed on the screen.</li>
		<li>Prompt the participant to adjust the size of the screen image to match the size of the physical object. Based on the measurements of a physical CD (diameter of 4.7&#8221;) or a credit card (width of 3.2&#8221;) in addition to the pixel size of the representative image, determine the ratio of pixels to inches for the screen.</li>
		<li>Retrieve the pixel resolution of the monitor via JavaScript&#8217;s screen.width and screen.height properties to then calculate the diagonal size of the screen in pixels. Knowing this value and the previously estimated pixel-to-inch ratio (see step 2.1.2), convert the diagonal size to inches. Have the participant confirm this value through a dialog box.</li>
	</ol>
	</li>
	<li>Prompt the participant to adjust the screen brightness settings until all 12 bands in a black-to-white gradient displayed on the screen are clearly distinguishable. Brightness setting controls vary by computer.</li>
	<li>Ask the participant to sit an arm&#8217;s length away from the monitor in a comfortable position and then set the browser window to full screen mode. The browser window must be in full screen mode to maximize the visual space used by the tasks and to remove any visual distractions, such as the browser toolbar and desktop taskbars.</li>
	<li>Knowing the resolution of the participant&#8217;s screen and the diagonal size of the monitor, use the web application to automatically calculate the pixels/degree conversion value, based on a 50 cm viewing distance. Resize the dimensions of the stimuli in the tasks using this value. All visual angle dimensions reported below are based on this assumed mean distance value from the monitor.</li>
	<li>Once calibration is complete, ask the participant to complete the two tasks described below. Choose the order of the tasks or randomly assign the order via the web application.</li>
</ol>
3. Multiple Object Tracking Task (MOT) &#8211; Figure 2
<ol>
	<li>Introduce and familiarize the participant with the MOT stimuli through a self-guided tutorial, seen at: <a href="http://brainandlearning.org/jove/MOT/practice.php" target="_blank">http://brainandlearning.org/jove/MOT/practice.php</a>. Ask the participant to read step-by-step instructions that demonstrate how the trials will work. Once the participant finishes reading the instructions, prompt the participant to go through the practice trials.
	<ol>
		<li>Setup the practice stimuli to consist of 8 dots at 0.8&#176; with a movement speed of 2&#176;/sec. Use the HTML5 requestAnimationFrame API to optimize browser animation at a frame rate of 60 Hz in order to control this stimulus motion.</li>
		<li>Ensure the dots move within the boundaries of a circle of 2&#176; eccentricity and a circle no larger than the height of the participant&#8217;s screen, without the instructions obscured.</li>
		<li>Set the dots to move in a random trajectory, where at each frame a dot has a 60% chance of changing direction by a maximum angle of 0.2&#176;. If a dot collides with another dot or the inner or outer radial limits, move the dot in the opposite direction.</li>
		<li>Prompt the participant to track the blue dots (varying between 1 and 2 dots per practice trial), with the yellow dots acting as distractors.</li>
		<li>After 2 sec, change the blue dots to yellow dots and continue to move them amongst the original yellow dots for another 4 sec. At the end of each trial, stop the dots and highlight one.</li>
		<li>Prompt the participant to respond via key press whether the highlighted dot was a tracked dot or a distractor dot. Next, prompt the participant to press the space bar to continue onto the next trial.</li>
		<li>After 3 consecutive correct trials, or a maximum of 6 trials, move the participant onto the full task.</li>
	</ol>
	</li>
	<li>Start the full MOT task for the participant. An example of the task can be found at: <a href="http://brainandlearning.org/jove/MOT" target="_blank">http://brainandlearning.org/jove/MOT</a>.
	<ol>
		<li>Setup the full task with 16 dots that move at 5&#176;/sec within the space between 2&#176; eccentricity and 10&#176; eccentricity. If the participant&#8217;s screen cannot fit a circle of 10&#176; eccentricity, use the maximum size the screen can contain instead.</li>
		<li>Have the participant complete a total of 45 trials: a mixture of 5 trials consisting of 1 tracked dot and 10 trials each consisting of 2 - 5 tracked dots. Match all other parameters to the practice trials (see steps 3.1.3 - 3.1.6).</li>
		<li>Record the participant&#8217;s response and response time once the dot is highlighted.</li>
		<li>For every 15 trials, suggest a break to the participant. At these breaks, display the participant&#8217;s performance (percent of correct trials) within the block on the screen.</li>
	</ol>
	</li>
</ol>
4. Moving from One Task to Another (Optional Step)
<ol>
	<li>Allow the participant to take a break between the two tasks. However, repeat steps 1 and 2 if the tasks are not completed during the same login session.</li>
</ol>
5. Useful Field of View Task (UFOV) &#8211; Figure 3
<ol>
	<li>Introduce and familiarize the participant with the UFOV stimuli through a self-guided tutorial, seen at: <a href="http://brainandlearning.org/jove/UFOV/practice.php" target="_blank">http://brainandlearning.org/jove/UFOV/practice.php</a>. Ask the participant to go through 4 stages of step-by-step instructions that demonstrate the two target stimuli that must be attended to during the task.
	<ol>
		<li>Set the central target stimulus as a 1&#176; smiley that flashes at the center of the screen with either long or short hair. Randomize the smiley&#8217;s hair length across trials.</li>
		<li>Set the peripheral target stimulus as a 1&#176; star that flashes at 4&#176; eccentricity at one of 8 locations around the circle (0&#176;, 45&#176;, 90&#176;, 135&#176;, 180&#176;, 225&#176;, 270&#176;, and 315&#176;). Randomize the location of the star across trials.</li>
		<li>Control stimulus duration via number of frames used for presentation time. Optimize frame refresh at about 17 msec per frame by using the HTML5 requestAnimationFrame API.</li>
		<li>To check if the expected presentation time was achieved, use JavaScript&#8217;s getTime() method to obtain the stimulus duration&#8217;s start time and end time based on the participant&#8217;s system clock. Calculate the measured presentation time from these two values and use this value for data analysis.</li>
		<li>For each practice trial, wait 500 msec before displaying the stimuli for approximately 200 msec (about 12 frames).</li>
		<li>Follow stimulus presentation with a noise mask composed of a randomly generated grayscale dot array for 320 msec (about 19 frames).</li>
		<li>For stage 1, only display the central target and then prompt the participant to respond via key press which hair length was displayed.</li>
		<li>For stage 2, only display the peripheral target and then prompt the participant to click on one of 8 radial lines, representing the 8 possible target locations, to indicate where the star appeared.</li>
		<li>For stage 3, display both central and peripheral target stimuli and then prompt the participant to provide responses to both the type of smiley and the location of the star. 
		NOTE: Participants can freely choose the order of these two responses.</li>
		<li>For stage 4, display both target stimuli in addition to peripheral distractors, and then prompt the participant to respond to both target stimuli. For the distractors, display 1&#176; squares presented at the remaining 7 locations at 4&#176; eccentricity, in addition to 8 more squares at 2&#176; eccentricity.</li>
		<li>After the participant&#8217;s response, show the participant feedback (a green checkmark for a correct answer or a red cross for an incorrect answer) for each target response after each trial.</li>
		<li>Move the participant onto the next practice stage after getting 3 consecutive correct trials. After stage 4, move the participant onto the full task.</li>
	</ol>
	</li>
	<li>Prompt the participant to start the full UFOV task. An example of the task can be found at: <a href="http://brainandlearning.org/jove/UFOV" target="_blank">http://brainandlearning.org/jove/UFOV</a>.
	<ol>
		<li>Present the same central stimulus as in the practice session (see step 5.1.1). Display the peripheral target at 7&#176; eccentricity at one of the previously mentioned 8 locations (see step 5.1.2). 24 distractor squares are also displayed at 3&#176; eccentricity, 5&#176; eccentricity, and the remaining 7&#176; eccentricity locations.</li>
		<li>Use a 3-down, 1-up staircase procedure to determine the presentation time of the stimuli: decrease the duration of stimuli after 3 consecutive correct trials and increase after each error trial.</li>
		<li>Before the first 3 reversals in the staircase, use a step size of 2 frames (approximately every 33 msec). After 3 reversals, use a step size of 1 frame. Vary the delay before the stimulus onset between 1 frame and 99 frames per trial, and keep the noise mask duration at 320 msec (about 19 frames). 
		NOTE: Reversals are the points at which the duration changes either from increasing to decreasing, or decreasing to increasing.</li>
		<li>End the task when one of three conditions is met: the staircase procedure reaches 8 reversals; the participant completes 10 consecutive trials at either the ceiling duration (99 frames) or the floor duration (1 frame); or the participant reaches a maximum of 72 trials.</li>
		<li>Record the participant&#8217;s response and response time for both the central stimulus and the peripheral stimulus.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Henrich, J., Heine, S. J., Norenzayan, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+weirdest+people+in+the+world.">The weirdest people in the world.</a> The Behavioral And Brain Sciences. 33, 61-135 (2010).</li><li>Buhrmester, M., Kwang, T., Gosling, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Amazon's+Mechanical+Turk:+A+new+source+of+inexpensive,+yet+high-quality,+data.">Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data.</a> Perspectives on Psychological Science. 6 (1), 3-5 (2011).</li><li>Goodman, J. K., Cryder, C. E., Cheema, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data+collection+in+a+flat+world:+the+strengths+and+weaknesses+of+mechanical+turk+samples.">Data collection in a flat world: the strengths and weaknesses of mechanical turk samples.</a> Journal of Behavioral Decision Making. 26 (3), 213-224 (2013).</li><li>Mason, W., Suri, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Conducting+behavioral+research+on+Amazon's+Mechanical+Turk.">Conducting behavioral research on Amazon's Mechanical Turk.</a> Behavior Research Methods. 44 (1), 1-23 (2012).</li><li>Crump, M. J., McDonnell, J. V., Gureckis, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+Amazon's+Mechanical+Turk+as+a+tool+for+experimental+behavioral+research.">Evaluating Amazon's Mechanical Turk as a tool for experimental behavioral research.</a> PLoS One. 8, e57410 (1371).</li><li>Lupyan, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+difficulties+of+executing+simple+algorithms:+why+brains+make+mistakes+computers+don't.">The difficulties of executing simple algorithms: why brains make mistakes computers don't.</a> Cognition. 129, 615-636 (2013).</li><li>Ball, K., Owsley, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+useful+field+of+view+test:+a+new+technique+for+evaluating+age-related+declines+in+visual+function.">The useful field of view test: a new technique for evaluating age-related declines in visual function.</a> J Am Optom Assoc. 64 (1), 71-79 (1993).</li><li>Pylyshyn, Z. W., Storm, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tracking+multiple+independent+targets:+Evidence+for+a+parallel+tracking+mechanism.">Tracking multiple independent targets: Evidence for a parallel tracking mechanism.</a> Spatial Vision. 3 (3), 179-197 (1988).</li><li>Dye, M. W. G., Bavelier, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+development+of+visual+attention+skills+in+school-age+children.">Differential development of visual attention skills in school-age children.</a> Vision Research. 50 (4), 452-459 (2010).</li><li>Green, C. S., Bavelier, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enumeration+versus+multiple+object+tracking:+The+case+of+action+video+game+players.">Enumeration versus multiple object tracking: The case of action video game players.</a> Cognition. 101 (1), 217-245 (2006).</li><li>Chengalur, S. N., Rodgers, S. H., Bernard, T. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chapter+1.+Kodak+Company.+Ergonomics+Design+Philosophy.">Chapter 1. Kodak Company. Ergonomics Design Philosophy.</a> Kodak&#8217;s Ergonomic Design for People at Work. , (2004).</li><li>Elze, T., Bex, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=P1-7:+Modern+display+technology+in+vision+science:+Assessment+of+OLED+and+LCD+monitors+for+visual+experiments.">P1-7: Modern display technology in vision science: Assessment of OLED and LCD monitors for visual experiments.</a> i-Perception. 3 (9), 621 (2012).</li><li>Elze, T., Tanner, T. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Temporal+Properties+of+Liquid+Crystal+Displays:+Implications+for+Vision+Science+Experiments.">Temporal Properties of Liquid Crystal Displays: Implications for Vision Science Experiments.</a> PLoS One. 7 (9), e44048 (2012).</li></ol>

This work was supported by the Swiss National Foundation (100014_140676), the National Science Foundation (1227168), and the National Eye Institute of the National Institutes of Health (P30EY001319).

Online data collection has a number of advantages over standard laboratory-based data collection. These include the potential to sample far more representative populations than the typical college undergraduate pool utilized in the field, and the ability to obtain far greater sample sizes in less time than it takes to obtain sample sizes that are an order of magnitude smaller in the lab1-6 (e.g., the data points collected from 1,700+ participants in the current paper were obtained in less than one wee...

Over the past five years there has been a surge of interest in the use of online behavioral data collection methods. While the vast majority of publications in the domain of psychology have utilized potentially non-representative subject populations1 (i.e., primarily college undergraduates) and often reasonably small sample sizes as well (i.e., typically in the range of tens of subjects), online methods offer the promise of far more diverse and larger samples. For instance, Amazon&#8217;s Mechanical Turk service has been the subject of a number of recent studies, both describing the characteristics of the &#8220;worker&#8221; population and the use of this population in behavioral research2-6.
However, one significant concern related to such methods is the relative lack of control over critical stimulus variables. For example, in most visual psychophysics tasks, stimuli are described in terms of visual angle. The calculation of visual angles requires precise measurements of viewing distance, screen size, and screen resolution. While these parameters are trivial to measure and control in a lab setting (where there is a known monitor and participants view stimuli while in a chin rest placed a known distance from the monitor), the same is not true of online data collection. In an online environment, not only will participants inevitably use a wide variety of monitors of different sizes with different software settings, they also may not have easy access to rulers/tape measures that would allow them to determine their monitor size or have the knowledge necessary to determine their software and hardware settings (e.g., refresh rate, resolution).
Here we describe a set of methods to collect data on two well-known tests of visual attention &#8211; the Useful Field of View (UFOV) paradigm7 and the multiple object tracking (MOT) task8 &#8211; while avoiding as much as possible the sources of variability that are inherent in online measurements. These tasks can be run by any participant with an internet connection and an HTML5 compatible browser. Participants who do not know their screen size are walked through a measurement process utilizing commonly available items of standard size (i.e., credit card/CD &#8211; see Figure 1).
Data on these two tasks were collected from over 1,700 participants in a Massive Online Open Course. Average performance of this online sample was highly consistent with results obtained in tightly controlled laboratory-based measures of the exact same tasks9,10. Our results are thus consistent with the growing body of literature demonstrating the efficacy of online data collection methods, even in tasks that require specific control over viewing conditions.

<table border="0" cellpadding="0" cellspacing="0" width="573">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Name of Reagent/ Equipment</td>
			<td style="width:71px;">Company</td>
			<td style="width:119px;">Catalog Number</td>
			<td style="width:167px;">Comments/Description</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Computer/tablet</td>
			<td style="width:71px;">N/A</td>
			<td style="width:119px;">N/A</td>
			<td>It must have an internet connection and an HTML5 compatible browser</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">CD or credit card</td>
			<td style="width:71px;">N/A</td>
			<td style="width:119px;">N/A</td>
			<td>May not be needed if participant already knows the monitor size</td>
		</tr>
	</tbody>
</table>

methods to test visual attention online

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative data samples than can typically be collected on college campuses. However, before such methods can be widely adopted, a number of technological challenges must be overcome &#8211; in particular in experiments where tight control over stimulus properties is necessary. Here we present methods for collecting performance data on two tests of visual attention. Both tests require control over the visual angle of the stimuli (which in turn requires knowledge of the viewing distance, monitor size, screen resolution, etc.) and the timing of the stimuli (as the tests involve either briefly flashed stimuli or stimuli that move at specific rates). Data collected on these tests from over 1,700 online participants were consistent with data collected in laboratory-based versions of the exact same tests. These results suggest that with proper care, timing/stimulus size dependent tasks can be deployed in web-based settings.

Outlier Removal
A total of 1,779 participants completed the UFOV task. Of those, 32 participants had UFOV thresholds that were greater than 3 standard deviations from the mean, suggesting that they were unable to perform the task as instructed. As such, the UFOV data from these participants were removed from the final analysis, leaving a total of 1,747 participants.
Data were obtained from 1,746 participants for the MOT task. Two participants had mean accuracy scores t...

Watch this Scientific Journal Video about Methods to Test Visual Attention Online at JoVE.com

Methods to Test Visual Attention Online

To replicate laboratory settings, online data collection methods for visual tasks require tight control over stimulus presentation. We outline methods for the use of a web application to collect performance data on two tests of visual attention.

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative ...

Research

JoVE Journal

Behavior

11.7K ビュー.  University of Rochester. To replicate laboratory settings, online data collection methods for visual tasks require tight control over stimulus presentation. We outline methods for the use of a web application to collect performance data on two tests of visual attention.

ビデオ: Methods to Test Visual Attention Online

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are deliberately planned, while others spontaneously occur below conscious awareness. The deliberate component of this dichotomy has been studied extensively in several contexts, while the spontaneous component remains largely under-explored. Moreover, how perceptual processes modulate these movement classes is still unclear. In particular, a currently debated issue is whether the visuomotor system is governed by the spatial percept produced by a visual illusion or whether it is not affected by the illusion and is governed instead by the veridical percept. Bistable percepts such as 3D depth inversion illusions (DIIs) provide an excellent context to study such interactions and balance, particularly when used in combination with reach-to-grasp movements. In this study, a methodology is developed that uses a DII to clarify the role of top-down processes on motor action, particularly exploring how reaches toward a target on a DII are affected in both deliberate and spontaneous movement domains.

It is unclear how top-down signals from the ventral visual stream affect movement. We developed a paradigm to test motor behavior towards a target on a 3D depth inversion illusion. Significant differences are reported in both deliberate, goal-directed movements and automatic actions under illusory and veridical viewing conditions.

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are ...

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

When viewers search for targets in a rapid serial visual presentation (RSVP) stream, if two targets are presented within about 500 msec of each other, the first target may be easy to spot but the second is likely to be missed. This phenomenon of attentional blink (AB) has been widely studied to probe the temporal capacity of attention for detecting visual targets. However, with the typical procedure of AB experiments, it is not possible to examine how the processing of non-target items in RSVP may be affected by attention. This paper describes a novel dual task procedure combined with RSVP to test effects of AB for nontargets at varied stimulus onset asynchronies (SOAs). In an exemplar experiment, a target category was first displayed, followed by a sequence of 8 nouns. If one of the nouns belonged to the target category, participants would respond &#8216;yes&#8217; at the end of the sequence, otherwise participants would respond &#8216;no&#8217;. Two 2-alternative forced choice memory tasks followed the response to determine if participants remembered the words immediately before or after the target, as well as a random word from another part of the sequence. In a second exemplar experiment, the same design was used, except that 1) the memory task was counterbalanced into two groups with SOAs of either 120 or 240 msec and 2) three memory tasks followed the sequence and tested remembrance for nontarget nouns in the sequence that could be anywhere from 3 items prior the target noun position to 3 items following the target noun position. Representative results from a previously published study demonstrate that our procedure can be used to examine divergent effects of attention that not only enhance targets but also suppress nontargets. Here we show results from a representative participant that replicated the previous finding.&#160;

This paper describes a novel dual task procedure combined with Rapid Serial Visual Presentation to look at attentional blink at varied stimulus onset asynchronies. This experimental procedure differed from others in that it tested attentional blink for nontargets.

When viewers search for targets in a rapid serial visual presentation (RSVP) stream, if two targets are presented within about 500 msec of each other, the ...

Visualizing Visual Adaptation

Many techniques have been developed to visualize how an image would appear to an individual with a different visual sensitivity: e.g., because of optical or age differences, or a color deficiency or disease. This protocol describes a technique for incorporating sensory adaptation into the simulations. The protocol is illustrated with the example of color vision, but is generally applicable to any form of visual adaptation. The protocol uses a simple model of human color vision based on standard and plausible assumptions about the retinal and cortical mechanisms encoding color and how these adjust their sensitivity to both the average color and range of color in the prevailing stimulus. The gains of the mechanisms are adapted so that their mean response under one context is equated for a different context. The simulations help reveal the theoretical limits of adaptation and generate &#34;adapted images&#34; that are optimally matched to a specific environment or observer. They also provide a common metric for exploring the effects of adaptation within different observers or different environments. Characterizing visual perception and performance with these images provides a novel tool for studying the functions and consequences of long-term adaptation in vision or other sensory systems.

This article describes a novel method for simulating and studying adaptation in the visual system.

Many techniques have been developed to visualize how an image would appear to an individual with a different visual sensitivity: e.g., because of optical ...

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The proposed method is based on a new and synergistic combination of three components: the temporal-order judgments (TOJ) paradigm, Bundesen&#39;s Theory of Visual Attention (TVA), and a hierarchical Bayesian estimation framework. The method provides readily interpretable parameters, which are supported by the theoretical and neurophysiological underpinnings of TVA. Using TOJs, TVA-based estimates can be obtained for a broad range of stimuli, whereas traditional paradigms used with TVA are mainly limited to letters and digits. Finally, the meaningful parameters of the proposed model allow for the establishment of a hierarchical Bayesian model. Such a statistical model allows assessing results in one coherent analysis both on the subject and the group level.
To demonstrate the feasibility and versatility of this new approach, three experiments are reported with attention manipulations in synthetic pop-out displays, natural images, and a cued letter-report paradigm.

Temporal-order judgments can be used to estimate processing speed parameters and attentional weights and thereby to infer the mechanisms of attentional processing. This methodology can be applied to a wide range of visual stimuli and works with many attention manipulations.

This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The ...

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Two dominant theories on lateralized processing of emotional information exist in the literature. One theory posits that unpleasant emotions are processed by right frontal regions, while pleasant emotions are processed by left frontal regions. The other theory posits that the right hemisphere is more specialized for the processing of emotional information overall, particularly in posterior regions.
Assessing the different roles of the cerebral hemispheres in processing emotional information can be difficult without the use of neuroimaging methodologies, which are not accessible or affordable to all scientists. Divided visual field presentation of stimuli can allow for the investigation of lateralized processing of information without the use of neuroimaging technology. 
This study compared central versus divided visual field presentations of emotional images to assess differences in motivated attention between the two hemispheres. The late positive potential (LPP) was recorded using electroencephalography (EEG) and event-related potentials (ERPs) methodologies to assess motivated attention. Future work will pair this paradigm with a more active behavioral task to explore the behavioral impacts on the attentional differences found.

This study compared central versus divided visual field presentations of emotional images to assess differences in motivated attention between the two hemispheres. The late positive potential (LPP) was recorded using electroencephalography (EEG) and event-related potentials (ERPs) methodologies to assess motivated attention.

Two dominant theories on lateralized processing of emotional information exist in the literature. One theory posits that unpleasant emotions are processed ...

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded via an eye tracker as the participant produces or comprehends a spoken language describing the concurrent visual world. This paradigm has high versatility, as it can be used in a wide range of populations, including those who cannot read and/or who cannot overtly give their behavioral responses, such as preliterate children, elderly adults, and patients. More importantly, the paradigm is extremely sensitive to fine grained manipulations of the speech signal, and it can be used to study the online processing of most topics in language comprehension at multiple levels, such as the fine grained acoustic phonetic features, the properties of words, and the linguistic structures. The protocol described in this article illustrates how a typical visual world eye tracking study is conducted, with an example showing how the online processing of some semantically complex statements can be explored with the visual world paradigm.

The visual world paradigm monitors participants' eye movements in the visual workspace as they are listening to or speaking a spoken language. This paradigm can be used to investigate the online processing of a wide range of psycholinguistic questions, including semantically complex statements, such as disjunctive statements.

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded ...

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact with people around them. Head-mounted eye tracking offers a unique opportunity to capture children's dynamic egocentric views and how they allocate visual attention within those views. This protocol provides guiding principles and practical recommendations for researchers using head-mounted eye trackers in both laboratory and more naturalistic settings. Head-mounted eye tracking complements other experimental methods by enhancing opportunities for data collection in more ecologically valid contexts through increased portability and freedom of head and body movements compared to screen-based eye tracking. This protocol can also be integrated with other technologies, such as motion tracking and heart-rate monitoring, to provide a high-density multimodal dataset for examining natural behavior, learning, and development than previously possible. This paper illustrates the types of data generated from head-mounted eye tracking in a study designed to investigate visual attention in one natural context for toddlers: free-flowing toy play with a parent. Successful use of this protocol will allow researchers to collect data that can be used to answer questions not only about visual attention, but also about a broad range of other perceptual, cognitive, and social skills and their development.

Gaze in Action: Head-mounted Eye Tracking of Children&#039;s Dynamic Visual Attention During Naturalistic Behavior

Young children do not passively observe the world, but rather actively explore and engage with their environment. This protocol provides guiding principles and practical recommendations for using head-mounted eye trackers to record infants' and toddlers' dynamic visual environments and visual attention in the context of natural behavior.

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact ...

Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View

The single Trier Social Stress Test (TSST) and the TSST for groups (TSST-G) are the most used protocols to experimentally induce psychosocial stress. These tests are based on uncontrollability and social-evaluative threat, inducing psychological and physiological consequences (e.g., anxiety, emotional states, salivary cortisol increases). Many quantitative experimental studies have investigated these stress inducers and these consequences. But, as far as we know, this study is the first to provide a qualitative analysis to access the participants' voices so as to understand the dynamics of their experience throughout the TSST and the TSST-G. This paper outlines a mixed methods approach to the TSST. This approach can help to maximize the information that can be gained from the TSST, which researchers often use without looking more closely at what is qualitatively happening psychologically for participants during the stressor itself. In this way, this protocol is an example of mixed methods, showing the added value of using the enactive phenomenological approach to analyze experimental protocols more deeply. This kind of mixed methods is helpful to access the experience, to understand the actor's point of view, and to analyze in-depth the dynamics of cognitive processes like intentions, perceptions, enacted knowledge, and emotion. The discussion section shows the different uses of a mixed methods protocol, exploiting the enactive phenomenological approach to analyze a protocol or to give a cross vision of the same research subject. This section deals with different existing applications, pointing out some critical steps in this mixed methods approach.

Here, we present an original mixed method combining a quantitative and a qualitative approach to examine accurately participants&#39; experiences during the Trier Social Stress Test and the Trier Social Stress Test for groups.

The single Trier Social Stress Test (TSST) and the TSST for groups (TSST-G) are the most used protocols to experimentally induce psychosocial stress. ...

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

Several models of the in-store search process exist in the fields of retailing, marketing, and consumer-based research. The present article presents a new conceptualization of this search process, which captures customers&#8217; visual attention at three distinct levels of analysis: Stock, Shelf, and Store. We refer to this conceptualization as the 3S Model and illustrate its usefulness through three eye-tracking studies, one from each level of analysis. Our experimental examples, which range from manipulating certain stimuli on a single product (e.g., the placement of textual and pictorial packaging elements) to manipulating the entire shopping trip for customers during their stay in a store (e.g., through more or less specific shopping tasks), highlight the broad applicability of this alternative approach for understanding customers&#8217; in-store search behavior. Thus, our model can be seen as a helpful tool for researchers interested in how to conduct experimental eye-tracking studies that shed light on the perceptual processes preceding product choices and purchase decisions. The 3S Model is equally suitable in controlled lab conditions and under ecologically valid settings in the real retail environment. Furthermore, it can be used from the micro level, with a focus on the meaningful metrics on a particular product, through the intermediate level, with the emphasis on the area surrounding products in shelves and other in-store spaces, all the way to the macro level, examining customers&#8217; navigational paths throughout a store as a function of their shopping tasks, cognitive capacity, or ability to acquire in-store information.

Spotlighting Customers&#039; Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

This article presents a new conceptualization of the in-store search process, the 3S Model, which captures customers&#8217; visual attention at three distinct levels of analysis: Stock, Shelf, and Store. We illustrate the usefulness of our conceptualization through three eye-tracking studies, one from each level of analysis in the 3S Model.

Several models of the in-store search process exist in the fields of retailing, marketing, and consumer-based research. The present article presents a new ...

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

The article describes a new methodology designed with the aim of finding a comprehensive, unobtrusive, and accurate way of capturing social recovery capital development in online communities of recovery from alcohol and drug (AOD) addiction. Recovery capital was conceptualised as both engagement in the online recovery community and identification with the community. To measure recovery capital development, naturally occurring data were extracted from the social media page of a specific recovery program, with the page being set up as a resource for a face-to-face recovery program. To map engagement with the online community, social network analysis (SNA) capturing online social interaction was performed. Social interaction was measured through the linkages between the online contributors/members of the online community as represented by program clients, staff, and supporters from the broader community. To capture markers of social identification with the online community, computerised linguistic analysis of the textual data (content from posts and comments) was conducted. Recovery capital captured in this way was analysed against retention data (a proxy outcome indicator), as days spent in the (face-to-face) recovery program. The online data extracted was linked to participant data in regards to program retention to test prediction of a key recovery outcome. This approach allowed the examination of the role of online support communities and assessment of the association between recovery capital (developed via the online community of recovery) and recovery outcomes.

The article describes a novel approach for analyzing dynamic online social interactions (in an online context) exemplified by a study of an online community of recovery from alcohol and drug addiction.

The article describes a new methodology designed with the aim of finding a comprehensive, unobtrusive, and accurate way of capturing social recovery ...